Method and system to predict ATM locations for users

ABSTRACT

A system and method for routing customers to an automated teller machine (ATM) is disclosed herein. A computing system receives, from a client device, a request to locate an ATM. The request includes a constraint of a desired ATM. The computing system identifies a plurality of ATMs proximate a location of the client device. The computing system pings the plurality of ATMs proximate the location of the client device to identify attributes associated with each respective ATM. The computing system receives the attributes from the plurality of ATMs proximate the location of the client device. The computing system compares the attributes from each respective ATM to historical ATM usage statistics associated with the client device. The computing system routes a user of the client device to a target ATM from the plurality of ATMs based at least partially on the historical ATM usage statistics.

FIELD OF THE DISCLOSURE

The present disclosure generally relates to a system and method forrouting customers to an automated teller machine (ATM).

BACKGROUND

Currently, there are various means in which consumers may transact withthird-party vendors. Credit card products are one instrument that areoffered and provided to consumers by credit card issuers (e.g., banksand other financial institutions). With a credit card, an authorizedconsumer is capable of purchasing services and/or merchandise without animmediate, direct exchange of cash. Rather, the consumer incurs debtwith each purchase. Debit cards are another type of instrument offeredand provided by banks (or other financial institutions) that areassociated with the consumer's bank account (e.g., checking account).Transactions made using a debit card are cleared directly from thecardholder's bank account. Still further, even in the digital age,consumers interact with automated teller machines (ATMs) to withdraw ordeposit physical banknotes. As such, despite reliance on digital ornon-cash transactions, there is still a need for individuals to locateATMs during their regular course of business. Conventional approaches toidentifying such ATMs may include searching for ATM locations via abank's website or an Internet search. Such approaches have limitationsin that they merely provide the location of ATMs, without providingadditional details of the features corresponding therewith. Accordingly,conventional approaches are simply unable to route users to an ATM basedon a particular user's ATM preferences or needs.

SUMMARY

Embodiments disclosed herein generally relate to a system and method forrouting customers to an automated teller machine (ATM). In oneembodiment, a method of routing customers to an ATM is disclosed herein.The computing system receives, from a client device, a request to locatean ATM. The request includes a constraint of a desired ATM. Thecomputing system identifies a plurality of ATMs proximate a location ofthe client device. The computing system pings the plurality of ATMsproximate the location of the client device to identify attributesassociated with each respective ATM. The computing system receives theattributes from the plurality of ATMs proximate the location of theclient device. The computing system compares the attributes from eachrespective ATM to historical ATM usage statistics associated with theclient device. The computing system routes a user of the client deviceto a target ATM from the plurality of ATMs based at least partially onthe historical ATM usage statistics.

In some embodiments, receiving, from the client device, the request tolocate the ATM includes the computing system receiving a text messagefrom the client device. The computing system analyzes the text messageto identify the request contained therein.

In some embodiments, the constraint of the desired ATM includes at leastone or more of a maximum distance of the ATM from the location of theclient device, a type of ATM, a fee-free ATM, an in-store ATM, and ano-limit ATM.

In some embodiments, the computing system receiving the attributes fromthe plurality of ATMs proximate the location of the client deviceincludes the computing system receiving, from each ATM, a total amountof funds available in the ATM.

In some embodiments, the computing system identifying the plurality ofATMs proximate the location of the client device includes the computingsystem identifying a first set of ATMs associated with the computingsystem. The computing system identifies a second set of ATMs notassociated with the computing system.

In some embodiments, the attributes associated with the ATMs include atleast one or more of a fee associated with withdrawal, an increment inwhich funds are withdrawn, and a location of the ATM.

In some embodiments, the computing system routing the user of the clientdevice to the target ATM from the plurality of ATMs based at leastpartially on the historical ATM usage statistics includes the computingsystem routing the user to the target ATM. The target ATM is scheduledto be refilled within a predefined period of the request.

In another embodiment, a method of routing customers to an automatedteller machine (ATM) is disclosed herein. A computing system identifiesthat a user is likely to transact at an ATM. The computing systemidentifies a current location of the user based on a current location ofa client device associated with the user. The computing system analyzesATM transaction habits of the user to identify a common attribute acrossthe plurality of ATMs with which the user transacted. The computingsystem identifies a plurality of candidate ATMs to which to route theuser for an ATM transaction. The computing system prompts the user tovisit a candidate ATM of the plurality of ATMs.

In some embodiments, the computing system identifying that the user islikely to transact at the ATM includes the computing system learning,via a prediction model, the frequency at which the user visits ATMs.

In some embodiments, the computing system identifying the plurality ofcandidate ATMs to which to route the user for the ATM transactionincludes the computing system identifying a favorite ATM of the user.The favorite ATM of the user is the ATM at which the user mostfrequently transacts. The computing system identifies attributes of thefavorite ATM of the user. The computing system determines that each ofthe plurality of candidate ATMs share at least one attribute of theattributes with the favorite ATM.

In some embodiments, the computing system identifying the plurality ofcandidate ATMs to which to route the user for the ATM transactionincludes the computing system pinging each of the plurality of candidateATMs to determine whether each of the plurality of candidate ATMs hassufficient funds for the ATM transaction.

In some embodiments, the computing system prompting the user to visitthe candidate ATM of the plurality of candidate ATMs includes thecomputing system generating a text message comprising the plurality ofATMs. The computing system transmits the text message to the clientdevice associated with the user.

In some embodiments, the computing system identifying the plurality ofcandidate ATMs to which to route the user for the ATM transactionincludes the computing system identifying a first set of candidate ATMsassociated with the computing system. The computing system identifies asecond set of candidate ATMs not associated with the computing system.

In some embodiments, the attributes include at least one or more of alocation of the ATM, a type of ATM, a maximum withdrawal amount, afee-type associated with the ATM, and an increment in which funds arewithdrawn.

In another embodiment, a system is disclosed herein. The system includesa processor and a memory. The memory has programming instructions storedthereon, which, when executed by the processor, perform one or moreoperations. The one or more operations include receiving, from a clientdevice, a request to locate an ATM. The request includes a constraint ofthe desired ATM. The one or more operations include identifying aplurality of ATMs proximate a location of the client device. The one ormore operations include pinging the plurality of ATMs proximate thelocation of the client device to identify attributes associated witheach respective ATM. The one or more operations include receiving theattributes from the plurality of ATMs proximate the location of theclient device. The one or more operations include comparing theattributes from each respective ATM to historical ATM usage statisticsassociated with the client device. The one or more operations includerouting a user of the client device to a target ATM from the pluralityof ATMs based at least partially on the historical ATM usage statistics.

In some embodiments, receiving, from the client device, the request tolocate the ATM includes receiving a text message from the client deviceand analyzing the text message to identify the request containedtherein.

In some embodiments, the constraint of the desired ATM includes at leastone or more of a maximum distance of the ATM from the location of theclient device, a type of ATM, a fee-free ATM, an in-store ATM, and ano-limit ATM.

In some embodiments, receiving the attributes from the plurality of ATMsproximate the location of the client device includes receiving, fromeach ATM, a total amount of funds available in the ATM.

In some embodiments, identifying the plurality of ATMs proximate thelocation of the client device includes identifying a first set of ATMsassociated with the computing system and identifying a second set ofATMs not associated with the computing system.

In some embodiments, the attributes associated with the ATMs comprise atleast one or more of a fee associated with withdrawal, an increment inwhich funds are withdrawn, and a location of the ATM.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features of the presentdisclosure can be understood in detail, a more particular description ofthe disclosure, briefly summarized above, may be had by reference toembodiments, some of which are illustrated in the appended drawings. Itis to be noted, however, that the appended drawings illustrated onlytypical embodiments of this disclosure and are therefore not to beconsidered limiting of its scope, for the disclosure may admit to otherequally effective embodiments.

FIG. 1 is a block diagram illustrating a computing environment,according to example embodiments.

FIG. 2 is a flow diagram illustrating a method of routing customers toan automated teller machine (ATM), according to example embodiments.

FIG. 3 is a block diagram illustrating a method of routing customers toan ATM, according to example embodiments.

FIG. 4 is a flow diagram illustrating a method of routing customers toan ATM, according to example embodiments.

FIG. 5 is a flow diagram illustrating a method of routing customers toan ATM, according to example embodiments.

FIG. 6A is a block diagram illustrating an exemplary client device,according to example embodiments.

FIG. 6B is a block diagram illustrating an exemplary client device,according to example embodiments.

FIG. 7 is a block diagram illustrating a computing environment,according to example embodiments.

To facilitate understanding, identical reference numerals have beenused, where possible, to designate identical elements that are common tothe figures. It is contemplated that elements disclosed in oneembodiment may be beneficially utilized on other embodiments withoutspecific recitation.

DETAILED DESCRIPTION

One or more techniques disclosed herein generally relate to a system andmethod of routing users to an ATM. For example, one or more techniquesdisclosed herein may attempt to route a user to an ATM based onhistorical transaction data associated with the user and one or moreparameters associated with an ATM request. When users attempt totransact at an ATM, users typically are unaware of the parametersassociated with an ATM. For example, a given ATM may be unable toreceive cash deposits, unable to receive check deposits, may be unableto allow withdrawals in intervals desired by the user, and the like.Still further, in some situations, a given ATM may not be able toaccommodate a user's withdrawal request due to inadequate funds in theATM.

The one or more techniques disclosed herein attempt to address the abovelimitations of conventional ATMs by routing users to certain ATMs basedon the user's transaction history and parameters associated with an ATM(e.g., usage statistics/modeling). For example, when the user requestsan ATM location, the user may submit with the request one or moreparameters of the desired ATM. The system may attempt to route the userto an ATM based on this request.

Further, in some embodiments, the system may predict when a user maynext transact at an ATM. For example, based on the user's transactionhistory, one or more techniques disclosed herein may identify when theuser may next use an ATM and anticipate such use by transmitting an ATMrecommendation to the user.

Further, in some embodiments, the predictive model may predict userdemand across the system of ATMs. For example, in addition to predictiveindividualized user demand, the predictive model may generate aprediction for demand across several ATMs and route users accordingly.Such predictive modeling may be beneficial in managing the fundsdistributed from each of the several ATMs. In other words, if a certainATM is identified as a “hot” or more transacted at ATM, the predictivemodeling may anticipate that ATM running out of funds before other ATMs.Rather than route a user to that particular ATM, the predictive modelmay divert users from that ATM and instead route the user to a morefunded ATM.

The term “user” as used herein includes, for example, a person or entitythat owns a computing device or wireless device; a person or entity thatoperates or utilizes a computing device; or a person or entity that isotherwise associated with a computing device or wireless device. It iscontemplated that the term “user” is not intended to be limiting and mayinclude various examples beyond those described.

FIG. 1 is a block diagram illustrating a computing environment 100,according to one embodiment. Computing environment 100 may include atleast a client device 102, ATMs 106, an organization computing system104, and a database 108 communicating via network 105.

Network 105 may be of any suitable type, including individualconnections via the Internet, such as cellular or Wi-Fi networks. Insome embodiments, network 105 may connect terminals, services, andmobile devices using direct connections, such as radio frequencyidentification (RFID), near-field communication (NFC), Bluetooth™,low-energy Bluetooth™ (BLE), Wi-Fi™ ZigBee™, ambient backscattercommunication (ABC) protocols, USB, WAN, or LAN. Because the informationtransmitted may be personal or confidential, security concerns maydictate one or more of these types of connection be encrypted orotherwise secured. In some embodiments, however, the information beingtransmitted may be less personal, and therefore, the network connectionsmay be selected for convenience over security.

Network 105 may include any type of computer networking arrangement usedto exchange data or information. For example, network 105 may be theInternet, a private data network, virtual private network using a publicnetwork and/or other suitable connection(s) that enables components incomputing environment 100 to send and receive information between thecomponents of system 100.

Client device 102 may be operated by a user. For example, client device102 may be a mobile device, a tablet, a desktop computer, or anycomputing system having the capabilities described herein. Client device102 may belong to or be provided to a user or may be borrowed, rented,or shared. Users may include, but are not limited to, individuals suchas, for example, subscribers, clients, prospective clients, or customersof an entity associated with organization computing system 104, such asindividuals who have obtained, will obtain, or may obtain a product,service, or consultation from an entity associated with organizationcomputing system 104.

Client device 102 may include at least application 112 and messagingapplication 111. Application 112 may be representative of a web browserthat allows access to a website or a stand-alone application. Clientdevice 102 may access application 112 to access the functionality oforganization computing system 104. Client device 102 may communicateover network 105 to request a webpage, for example, from web clientapplication server 114 of organization computing system 104. Forexample, client device 102 may be configured to execute application 112to access content managed by web client application server 114. Thecontent that is displayed to client device 102 may be transmitted fromweb client application server 114 to client device 102, and subsequentlyprocessed by application 112 for display through a graphical userinterface (GUI) of client device 102. In some embodiments, a user ofclient device 102 may access application 112 to identify the location ofan ATM.

Messaging application 111 may be representative of an application thatallows users to transmit electronic messages to one or more computingsystems (e.g., client devices). In some embodiments, client device 102may be configured to execute messaging application 111 to access anemail account managed by a third party web server. In some embodiments,client device 102 may be configured to execute messaging application 111to transmit one or more text messages (e.g., SMS messages, iMessages,etc.) to one or more remote computing devices.

Organization computing system 104 may include at least web clientapplication server 114, machine learning module 116, handler 118,natural language processor (NLP) device 120, and chat bot 122. Each ofmachine learning module 116, handler 118, NLP device 120, and chat bot122 may include one or more software modules. The one or more softwaremodules may be collections of code or instructions stored on a media(e.g., memory of organization computing system 104) that represent aseries of machine instructions (e.g., program code) that implements oneor more algorithmic steps. Such machine instructions may be the actualcomputer code the processor of organization computing system 104interprets to implement the instructions or, alternatively, may be ahigher level of coding of the instructions that is interpreted to obtainthe actual computer code. The one or more software modules may alsoinclude one or more hardware components. One or more aspects of anexample algorithm may be performed by the hardware components (e.g.,circuitry) itself, rather as a result of an instructions.

In some embodiments, machine learning module 116 may be configured tolearn user ATM habits. For example, machine learning module 116 may beconfigured to learn how often a user transacts at an ATM, the ATMs atwhich the user transacts, the types of ATMs with which the userinteracts, user transaction habits at ATMs, and the like. Machinelearning module 116 may include one or more instructions to train aprediction model. To train the prediction model, machine learning module116 may receive, as input, ATM transaction data. Such ATM transactiondata may include at least one or more of a plurality of ATMtransactions, the users associated with each transaction, anonymizeddata, one or more attributes of the ATM involved in each transaction,and the like. Machine learning module 116 may implement one or moremachine learning algorithms to train the prediction model to identify anATM to which organization computing system 104 may route the user. Forexample, machine learning module 116 may train a prediction model toidentify one or more ATM habits of an individual and one or moreattributes of each ATM visited by the user to identify user preferenceson ATM use. In some embodiments, the prediction model may generate aplurality of ATM options, allowing the user to choose the ATM at whichthe user wishes to transact. Still further, in some embodiments,prediction model may generate a prediction of when the user will nexttransact at an ATM and generate a proposal for the user in anticipationof this next transaction.

Machine learning module 116 may use one or more of a decision treelearning model, association rule learning model, artificial neuralnetwork model, deep learning model, inductive logic programming model,support vector machine model, clustering mode, Bayesian network model,reinforcement learning model, representational learning model,similarity and metric learning model, rule-based machine learning model,and the like to train the prediction model.

Chat bot 122 may be configured to communicate with client device 102.For example, chat bot 122 may be configured to receive one or moremessages from client device 102. In some embodiments, chat bot 122 maybe configured to establish a persistent chat session between clientdevice 102 and organization computing system 104. Additionally, chat bot122 may engage in dialogue with a user of client device 102, such thatchat bot 122 may respond to any follow-up questions the user may have.For example, chat bot 122 may provide the user with one or more ATMrecommendations, based on an output generated by the prediction model.

NLP device 120 may be configured to receive and process incomingdialogue messages from client device 102. For example, NLP device 120may be configured to receive a request from client device 102 forlocating a nearby ATM based on user preferences. NLP device 120 may beconfigured to receive and execute a command that includes the request,where the command instructs NLP device 120 to work in conjunction withmachine learning module 116 to route the user to a particular ATM. NLPdevice 120 may be configured to continuously monitor or intermittentlylisten for and receive commands to determine if there are any newcommands or requests directed to NLP device 120. Upon receiving andprocessing an incoming dialogue message, NLP device 120 may output themeaning of the request, for example, in a format that other componentsof organization computing system 104 can process. In some embodiments,the received dialogue message may be the result of client device 102transmitting a text message to organization computing system 104. Insome embodiments, the received dialogue message may be the result ofclient device 102 transmitting an electronic message to organizationcomputing system 104. In some embodiments, client device 102 maytransmit a request via application 112 executing thereon.

In some embodiments, handler 118 may be configured to manage an accountassociated with each user. For example, account handler 118 may beconfigured to communicate with database 108. As illustrated, database108 may include one or more user profiles 126 and one or more ATMs 128.Each user profile 126 may correspond to a user with an account withorganization computing system 104. Each user profile 126 may include oneor more transactions 130, personal identification information 132, andone or more ATM interactions 134.

Each of one or more transactions 130 may correspond to the transactionassociated with an account of the user. Such transactions may include,but are not limited to, checking account transactions, savings accounttransactions, credit card transactions, ATM card transactions, transfertransactions, and the like. Personal identification information 132 maycorrespond to one or more items of information associated with the user.Such personal identification information 132 may include but is notlimited to, user name, password, date of birth, social security number,address, full legal name, telephone number, billing zip code, salaryinformation, and the like. ATM interactions 134 may correspond to ahistory of ATMs visited by the user. For example, for each ATMtransaction in transactions 130, handler 118 may log a unique identifierassociated with said ATM. In some embodiments, handler 118 may furtherlog one or more features that the user leveraged during the ATMtransaction (e.g., withdrawal amount, withdrawal denominations,deposited cash, deposited checks, checked balance, requested a receipt,and the like). Handler 118 may be configured to identify or log thesefeatures for all ATM transaction. In other words, handler 118 may beconfigured to log these features for ATMs associated with organizationcomputing system 104 and ATMs not associated with organization computingsystem 104. Accordingly, database 108 may include a full ATM history ofthe user.

Each ATM 128 may be representative of an ATM associated withorganization computing device. Each ATM 128 may include location 136,attributes 138, and funds 140. Location 136 may correspond to thephysical location of the respective ATM 128. Location 136 may berepresentative of an address associated with ATM 128, locationcoordinates associated with ATM 128, a type of location in which ATM 128is located (e.g., bank, grocery store, liquor store, mall, etc.), andthe like.

Attributes 138 may correspond to one or more capabilities or traitsassociated with each ATM 128. Such attributes 138 may include but arenot limited to, stamp purchases, no fee transactions, choose-your-owncash denomination, hearing-impaired capabilities, vision-impairedcapabilities, and the like.

Funds 140 may correspond to an amount of funds currently available ineach ATM 106. For example, handler 118 may ping a controller 124 of eachATM 106 to receive an updated amount of funds associated with each ATM.In some embodiments, handler 118 may maintain a running total of fundsassociated with each ATM 106, which may be updated after eachtransaction at the respective ATM 106. By identifying the amount offunds 140 available in each ATM 106, the prediction model may be able toaccount for those ATMs that do not have sufficient funds or do not havecertain denominations the user typically requests. In some embodiments,handler 118 may maintain a running total of funds associated withthird-party ATM 106 via one or more application programming interfaces(APIs) that link organizations associated third-party ATMs toorganization computing system 104. In some embodiments, handler 118 maymaintain a running total of funds associated with third-party ATMs byinferring said information based on ATM interactions 134 in database108. In some embodiments, handler 118 may maintain a running total offunds associated with third-party ATMs 106 via one or more APIs thatconnect organization computing system 104 with a third party servicethat maintains said information.

FIG. 2 is a flow diagram illustrating a method 200 of routing customersto an automated teller machine (ATM), according to example embodiments.Method 200 may begin at step 202.

At step 202, organization computing system 104 may receive a request tolocate an ATM. In some embodiments, organization computing system 104may receive a request to locate an ATM via application 112 executing onclient device 102. For example, a user may navigate to application 112executing on client device 102 to locate one or more nearby ATMs. Insome embodiments, organization computing system 104 may receive arequest to locate an ATM via messaging application 111 executing onclient device 102. For example, a user may transmit a text message tochat bot 122, requesting a nearby ATM. Upon receiving the text message,NLP device 120 may parse the text message to understand the requestcontained therein.

At step 204, organization computing system 104 may prompt a user toinput one or more parameters of a desired ATM. In some embodiments,organization computing system 104 may prompt the user to provide one ormore parameters via application 112 executing on client device 102. Forexample, organization computing system 104 may request that the userconstrain his or her ATM search based on various constraints. In someembodiments, organization computing system 104 may prompt the user toconstrain his or her ATM search by transmitting one or more textmessages to client device 102. For example, chat bot 122 may generate aseries of questions for transmission to client device 102, prompting theuser with various questions directed to a type of ATM. Such questionsmay include, but are not limited to, “No fee only ATMs?,”“Deposit-capable ATMs?,” and the like. In some embodiments, organizationcomputing system 104 may pre-populate search constraints for the userbased on previous interactions with machine learning model 116 andtransmit such constraints to client device 102 for review and/orediting.

At step 206, organization computing system 104 may receive one or moreparameters from client device 102. For example, organization computingsystem 104 may receive one or more parameters from client device 102 viaapplication 112 or messaging application 111.

At step 208, organization computing system 104 may identify one or moreATMs within a radius specified by the user. For example, handler 118 maybe configured to query database 108 with the one or more parametersreceived from client device 102 to identify one or more ATMs thatsatisfy the user's request. In some embodiments, handler 118 may querydatabase 108 and not receive any results. In such case, handler 118 maybroaden the query by selectively removing parameters from the one ormore parameters.

At step 210, organization computing system 104 may query database 108 toidentify one or more attributes associated therewith. In someembodiments, organization computing system 104 may ping one or more ATMs106 associated with organization computing system 104 to verify thestatus. For third-party ATMs, database 108 may maintain one or moreattributes associated therewith based on customer historicalinteractions. For example, if anyone withdrew $50 from a third-partyATM, database 108 may reflect such transactions. This may mean that saidthird-party ATM allows for withdrawal denominations under $20.

At step 214, organization computing system 104 may route the user to atleast one of the one or more ATMs. For example, based on the one or moreattributes received from the one or more ATMs, organization computingsystem 104 may provide the user with an ATM recommendation. In someembodiments, the ATM recommendation may include two or more ATMs thatsatisfy a criteria specified by the user. In some embodiments, the ATMrecommendation may include a single ATM recommendation. In someembodiments, the ATM recommendation may include an ATM that does notsatisfy the user's criteria (i.e., no ATMs satisfying that criteria werefound). Organization computing system 104 may transmit therecommendation to client device 102. In some embodiments, organizationcomputing system 104 may notify the user via application 112 executingon client device 102. In some embodiments, organization computing system104 may notify the user via an electronic message (e.g., text message).In some embodiments, the recommendation may include a link to open a mapprogram having directions to the ATM. In some embodiments, therecommendation may include an address of the ATM. Further, in someembodiments, organization computing system 104 may transmit a textmessage to the user with the recommendation. In some embodiments,organization computing system 104 may send a notification to clientdevice 102 via application 112 executing thereon.

FIG. 3 is a block diagram illustrating operations associated withrouting a user to an ATM, according to example embodiments. At operation302, client device 102 may transmit an ATM request to organizationcomputing system 104. In some embodiments, the ATM request may betransmitted via application 112 executing on client device 102. Forexample, a user may navigate to an ATM finder feature, that allows auser to query organization computing system 104 for an ATM. In someembodiments, the ATM request may be transmitted to organizationcomputing system 104 via an electronic message. For example, a user ofclient device 102 may use messaging application 111 to transmit a textmessage to organization computing system.

At operation 304, organization computing system 104 may receive therequest from client device 102. In some embodiments, organizationcomputing system 104 may receive a request to locate an ATM viaapplication 112 executing on client device 102. In some embodiments,organization computing system 104 may receive a request to locate an ATMvia messaging application 111 executing on client device 102. Forexample, a user may transmit a text message to chat bot 122, requestinga nearby ATM. Upon receiving the text message, NLP device 120 may parsethe text message to understand the request contained therein.Organization computing system 104 may prompt a user to input one or moreparameters of a desired ATM. Such parameters may include an amount to bewithdrawn, a desired denomination, an ATM that sells stamps, ahearing-impaired ATM, and the like. In some embodiments, organizationcomputing system 104 may prompt the user to provide one or moreparameters via application 112 executing on client device 102. Forexample, organization computing system 104 may request that the userconstrain his or her ATM search based on various constraints. In someembodiments, organization computing system 104 may prompt the user toprovide one or more parameters for his or her ATM search by transmittingone or more text messages to client device 102.

At operation 306, client device 102 may transmit the requested one ormore parameters of a desired ATM. In some embodiments, client device 102may provide the one or more parameters via application 112. For example,a user may select or submit a set of constraints via application 112. Insome embodiments, client device 102 may provide the one or moreparameters to organization computing system 104 via messagingapplication 111.

At operation 308, organization computing system 104 may receive the setof constraints from client device 102. Organization computing system 104may identify one or more ATMs within a radius specified by the user. Forexample, handler 118 may be configured to query database 108 with theone or more parameters received from client device 102 to identify oneor more ATMs that satisfy the user's request.

At step 310, organization computing system 104 may query database 108 toidentify one or more attributes associated therewith. In someembodiments, organization computing system 104 may ping (orcommunication with) one or more ATMs 106 associated with organizationcomputing system 104 to verify the status. For example, organizationcomputing system 104 may use machine learning model 116 to learn auser's ATM habits, and ping (or communicate with) ATM 106 to determineif it can support the identified ATM habits. For third-party ATMs,database 108 may maintain one or more attributes associated therewithbased on customer historical interactions. For example, if anyonewithdrew $50 from a third-party ATM, database 108 may reflect suchtransactions. This may mean that said third-party ATM allows forwithdrawal denominations under $20. In some embodiments, block diagram300 may include operation 312. At operation 312, each ATM 106 mayreceive the request from organization computing system 104. Each ATM 106may process the request and transmit the requested information back toorganization computing system 104.

At operation 314, organization computing system 104 may identify atleast one ATM to route the user. For example, based on the one or moreattributes received from the one or more ATMs, organization computingsystem 104 may identify a subset of ATMs based on the informationidentified at operation 310. In some embodiments, organization computingsystem 104 may identify two or more ATMs that satisfy a criteriaspecified by the user. In some embodiments, organization computingsystem 104 may identify a single ATM. In some embodiments, organizationcomputing system 104 may not identify any ATM, because none of the ATMssatisfy the user's criteria (i.e., no ATMs satisfying that criteria werefound).

At step 316, organization computing system 104 may transmit therecommendation to client device 102. In some embodiments, organizationcomputing system 104 may notify the user via application 112 executingon client device 102. In some embodiments, organization computing system104 may notify the user via an electronic message (e.g., text message).

FIG. 4 is a flow diagram illustrating a method 400 of routing a user toan ATM, according to example embodiments. Method 400 may begin at step402.

At step 402, organization computing system 104 may identify one or moreATMs to be refilled within a predefined time range. For example, handler118 may look up what the maintenance schedule is for one or more ATMs.In some embodiments, handler 118 may look up the maintenance schedulevia one or more APIs. In some embodiments, handler 118 may look up themaintenance schedule via database 108.

At step 404, organization computing system 104 may receive a request tolocate an ATM. In some embodiments, organization computing system 104may receive a request to locate an ATM via application 112 executing onclient device 102. For example, a user may navigate to application 112executing on client device 102 to locate one or more nearby ATMs. Insome embodiments, organization computing system 104 may receive arequest to locate an ATM via messaging application 111 executing onclient device 102.

At step 406, organization computing system 104 may identify a locationof client device 102. In some embodiments, organization computing system104 may identify the location of client device 102 by interfacing withapplication 112. For example, a user may have granted application 112location services, such that application 112 can identify a currentlocation of the user when application 112 is in use. In someembodiments, organization computing system 104 may prompt the user tosubmit location information via application 112. In some embodiments,organization computing system 104 may prompt the user to submit locationinformation via messaging application 111.

At step 408, organization computing system 104 may identify at least oneof the one or more ATMs within a predefined radius of client device 102.For example, organization computing system 104 may identify one or moreATMs 106 that will be refilled soon. In some embodiments, soon may meanthe next x-minutes, y-hours, z-days, and the like. Organizationcomputing system 104 may identify those ATMs 106 via the pollingoperation discussed above in conjunction with step 402.

At step 410, organization computing system 104 may route the user to atleast one of the one or more ATMs. For example, based on the refillinformation received from the one or more ATMs, organization computingsystem 104 may provide the user with an ATM recommendation. In someembodiments, the ATM recommendation may include two or more ATMs. Insome embodiments, the ATM recommendation may include a single ATMrecommendation. Organization computing system 104 may transmit therecommendation to client device 102. In some embodiments, organizationcomputing system 104 may notify the user via application 112 executingon client device 102. In some embodiments, organization computing system104 may notify the user via an electronic message (e.g., text message).

FIG. 5 is a flow diagram illustrating a method 400 of routing a user toan ATM, according to example embodiments. Method 500 may begin at step502.

At step 502, organization computing system 104 may analyze one or moreATM transactions of an individual. For example, organization computingsystem 104 may provide, as input, to machine learning module 116, astream of transactions 130 associated with a particular user. Machinelearning module 116 may leverage a trained prediction model to predictwhen the user will next transact at an ATM. In other words, organizationcomputing system 104 may leverage a prediction model to forecast thenext time a user is expected to withdraw or deposit funds at an ATM.

At step 504, organization computing system 104 may determine that theuser is likely to visit an ATM. For example, based on the user'shistorical transactions 130, machine learning module 116 may generate adate or time at which the user is predicted to utilize an ATM.

At step 506, organization computing system 104 may identify the locationof client device 102 by interfacing with application 112. For example, auser may have granted application 112 location services, such thatapplication 112 can identify a current location of the user whenapplication 112 is in use. In some embodiments, organization computingsystem 104 may prompt the user to submit location information viaapplication 112. In some embodiments, organization computing system 104may prompt the user to submit location information via messagingapplication 111.

At step 508, organization computing system 104 may identify at least oneATM 106 within a predefined radius of client device 102. For example,based on the identified location of client device 102, handler 118 mayquery database 108 to identify one or more ATMs 106 proximate clientdevice 102.

At step 510, organization computing system 104 may route the user to atleast one ATM 106. In some embodiments, the ATM recommendation mayinclude two or more ATMs. In some embodiments, the ATM recommendationmay include a single ATM recommendation. For example, organizationcomputing system 104 may generate an ATM recommendation by selecting anATM that has the capabilities for whatever type of interaction machinelearning model 116 predicts the customer may have with a given ATM(e.g., withdrawal of bills under $20). Organization computing system 104may transmit the recommendation to client device 102. In someembodiments, organization computing system 104 may notify the user viaapplication 112 executing on client device 102. In some embodiments,organization computing system 104 may notify the user via an electronicmessage (e.g., text message).

FIG. 6A is a block diagram 600 illustrating an exemplary client device602, according to example embodiments. Client device 602 may be similarto client device 102.

As illustrated, client device 602 can include screen 604. Screen 604 maybe displaying graphical user interface (GUI) 606. GUI 606 may capturethe interaction of client device 602 and organization computing system104 via messaging application 111.

As illustrated, messaging application 111 may include a first message610 and a second message 612. First message 610 may be transmitted fromclient device 602 to organization computing system 104. First message610 may recite: “Hi, can you please find me the closest ATM′?” In otherwords, first message 610 may be a request from client device 602 toorganization computing system 104 for an ATM location. Upon receivingthe request from client device 602, organization computing system, 104may route the user to at least one ATM 106 via, for example, theoperations discussed above in conjunction with FIGS. 2-4.

Second message 612 may include ATM recommendations generated byorganization computing system 104. As illustrated, the recommendationincludes three ATMs-ATM #1, ATM #2, and ATM #3. Along with each ATM inthe recommendation, organization computing system 104 may includeinformation about the ATM. For example, as illustrated, organizationcomputing system 104 may include distance information and attributeinformation in message 612.

FIG. 6B is a block diagram 650 illustrating an exemplary client device602, according to example embodiments. Client device 602 may be similarto client device 102.

As illustrated, client device 602 includes screen 604. Screen 604 may bedisplaying graphical user interface (GUI) 656. GUI 656 may capture theinteraction of client device 602 and organization computing system 104via messaging application 111.

As illustrated, messaging application 111 may include a first message660, a second message 662, and a third message 664. First message 660may be transmitted from organization computing system 104 to clientdevice 602. First message 660 may be generated responsive toorganization computing system 104 predicting that a user is likely totransact at an ATM in the near future (e.g., the current day, currentweek, etc.). For example, first message 660 may recite: “Hi, will you befrequenting an ATM today?”

Second message 662 may be transmitted from client device 602 toorganization computing system 104. Second message 662 may include aconfirmation that the user will be frequenting an ATM today. Forexample, second message 662 may recite “yes.” Upon receiving secondmessage 662, organization computing system 104 may use NLP device 120 toprocess the message.

Third message 664 may be transmitted from organization computing system104 to client device 102. Third message 664 may include an ATMrecommendation for the user, based, for example, on ATMs frequented bythe user. Third message 664 may recite: “Ok. Your most frequented ATM iscurrently low on funds right now and is unable to distribute funds overthe amount of $100. Would you like to see other ATMs in the area?”

FIG. 7 is a block diagram illustrating an exemplary computingenvironment 700, according to some embodiments. Computing environment700 includes computing system 702 and computing system 752. Computingsystem 702 may be representative of client device 102. Computing system752 may be representative of organization computing system 104.

Computing system 702 may include a processor 704, a memory 706, astorage 708, and a network interface 710. In some embodiments, computingsystem 702 may be coupled to one or more I/O device(s) 712 (e.g.,keyboard, mouse, etc.).

Processor 704 may retrieve and execute program code 720 (i.e.,programming instructions) stored in memory 706, as well as stores andretrieves application data. Processor 704 may be included to berepresentative of a single processor, multiple processors, a singleprocessor having multiple processing cores, and the like. Networkinterface 710 may be any type of network communications allowingcomputing system 702 to communicate externally via computing network705. For example, network interface 710 is configured to enable externalcommunication with computing system 752.

Storage 708 may be, for example, a disk storage device. Although shownas a single unit, storage 708 may be a combination of fixed and/orremovable storage devices, such as fixed disk drives, removable memorycards, optical storage, network attached storage (NAS), storage areanetwork (SAN), and the like.

Memory 706 may include application 716, operating system 718, programcode 720, and messaging application 724. Program code 720 may beaccessed by processor 704 for processing (i.e., executing programinstructions). Program code 720 may include, for example, executableinstructions for communicating with computing system 752 to display oneor more pages of website 764. Application 716 may enable a user ofcomputing system 702 to access a functionality of computing system 752.For example, application 716 may access content managed by computingsystem 752, such as website 764. The content that is displayed to a userof computing system 702 may be transmitted from computing system 752 tocomputing system 702, and subsequently processed by application 716 fordisplay through a graphical user interface (GUI) of computing system702.

Messaging application 722 may be representative of a web browser thatallows access to a website or a stand-alone application. In someembodiments, computing system 702 may be configured to execute messagingapplication 722 to access an email account managed by a third party webserver. In some embodiments, computing system 702 may be configured toexecute messaging application 722 to transmit one or more text messages(e.g., SMS messages, iMessages, etc.) to one or more remote computingdevices.

Computing system 752 may include a processor 754, a memory 756, astorage 758, and a network interface 760. In some embodiments, computingsystem 752 may be coupled to one or more I/O device(s) 762. In someembodiments, computing system 752 may be in communication with database108.

Processor 754 may retrieve and execute program code 768 (i.e.,programming instructions) stored in memory 756, as well as stores andretrieves application data. Processor 754 is included to berepresentative of a single processor, multiple processors, a singleprocessor having multiple processing cores, and the like. Networkinterface 760 may be any type of network communications enablingcomputing system 752 to communicate externally via computing network705. For example, network interface 760 allows computing system 752 tocommunicate with computer system 702.

Storage 758 may be, for example, a disk storage device. Although shownas a single unit, storage 758 may be a combination of fixed and/orremovable storage devices, such as fixed disk drives, removable memorycards, optical storage, network attached storage (NAS), storage areanetwork (SAN), and the like.

Memory 756 may include website 764, operating system 766, program code768, machine learning module 770, handler 772, NLP device 774, and chatboy 776. Program code 768 may be accessed by processor 754 forprocessing (i.e., executing program instructions). Program code 768 mayinclude, for example, executable instructions configured to performsteps discussed above in conjunction with FIGS. 2-5. As an example,processor 754 may access program code 768 to perform operations relatedto routing a user to an ATM. In another example, processor 754 mayaccess program code 768 to facilitate an ATM recommendation. Website 764may be accessed by computing system 702. For example, website 764 mayinclude content accessed by computing system 702 via a web browser orapplication.

Machine learning module 770 may be configured to learn user ATM habits.For example, machine learning module 770 may be configured to learn howoften a user transacts at an ATM, the ATMs at which the user transacts,the types of ATMs with which the user interacts, user transaction habitsat ATMs, and the like. Machine learning module 770 may include one ormore instructions to train a prediction model. To train the predictionmodel, machine learning module 770 may receive, as input, ATMtransaction data. Such ATM transaction data may include a plurality ofATM transactions, the users associated with each transaction, one ormore attributes of the ATM involved in each transaction, and the like.Machine learning module 770 may implement one or more machine learningalgorithms to train the prediction model to identify an ATM to whichcomputing system 772 may route the user. For example, machine learningmodule 770 may train a prediction model to identify one or more featuresATM habits of an individual and one or more attributes of each ATMvisited by the user to identify user preferences on ATM use. In someembodiments, the prediction model may generate a plurality of ATMoptions, allowing the user to choose the ATM at which the user wishes totransact. Still further, in some embodiments, the prediction model maygenerate a prediction of when the user will next transact at an ATM andgenerate a proposal for the user in anticipation of this nexttransaction.

Machine learning module 770 may use one or more of a decision treelearning model, association rule learning model, artificial neuralnetwork model, deep learning model, inductive logic programming model,support vector machine model, clustering mode, Bayesian network model,reinforcement learning model, representational learning model,similarity and metric learning model, rule-based machine learning model,and the like to train the prediction model.

Chat bot 776 may be configured to communicate with computing system 702.For example, chat bot 776 may be configured to receive one or moremessages from computing system 702. In some embodiments, chat bot 776may be configured to establish a persistent chat session betweencomputing system 702 and computing system 704. Additionally, chat bot776 may engage in dialogue with a user of computing system 702, suchthat chat interface 776 may respond to any follow-up questions the usermay have. For example, chat bot 776 may provide the user with one ormore ATM recommendations, based on an output generated by the predictionmodel.

NLP device 776 may be configured to receive and process incomingdialogue messages from computing system 702. For example, NLP device 776may be configured to receive a request from computing system 702 forlocating a nearby ATM based on user preferences. NLP device 776 may beconfigured to receive and execute a command that includes the request,where the command instructs NLP device 776 to work in conjunction withmachine learning module 770 to route the user to a particular ATM. NLPdevice 776 may be configured to continuously monitor or intermittentlylisten for and receive commands to determine if there are any newcommands or requests directed to NLP device 776. Upon receiving andprocessing an incoming dialogue message, NLP device 776 may output themeaning of the request, for example, in a format that other componentsof computing system 704 can process. In some embodiments, the receiveddialogue message may be the result of computing system 702 transmittinga text message to computing system 704. In some embodiments, thereceived dialogue message may be the result of computing system 702transmitting an electronic message to computing system 704.

Handler 772 may be configured to manage an account associated with eachuser. For example, account handler 772 may be configured to communicatewith database 108.

While the preceding is directed to embodiments described herein, otherand further embodiments may be devised without departing from the basicscope thereof. For example, aspects of the present disclosure may beimplemented in hardware or software or a combination of hardware andsoftware. One embodiment described herein may be implemented as aprogram product for use with a computer system. The program(s) of theprogram product defines functions of the embodiments (including themethods described herein) and can be contained on a variety ofcomputer-readable storage media. Illustrative computer-readable storagemedia include, but are not limited to: (i) non-writable storage media(e.g., read-only memory (ROM) devices within a computer, such as CD-ROMdisks readable by a CD-ROM drive, flash memory, ROM chips, or any typeof solid-state non-volatile memory) on which information is permanentlystored; and (ii) writable storage media (e.g., floppy disks within adiskette drive or hard-disk drive or any type of solid-staterandom-access memory) on which alterable information is stored. Suchcomputer-readable storage media, when carrying computer-readableinstructions that direct the functions of the disclosed embodiments, areembodiments of the present disclosure.

It will be appreciated to those skilled in the art that the precedingexamples are exemplary and not limiting. It is intended that allpermutations, enhancements, equivalents, and improvements thereto areapparent to those skilled in the art upon a reading of the specificationand a study of the drawings are included within the true spirit andscope of the present disclosure. It is therefore intended that thefollowing appended claims include all such modifications, permutations,and equivalents as fall within the true spirit and scope of theseteachings.

What is claimed:
 1. A method of routing customers to an automated tellermachine (ATM), comprising: retrieving, by a computing system associatedwith at least one financial institution having an ATM network,historical ATM transaction data for a plurality of users of the ATMnetwork, wherein the historical ATM transaction data comprises, for eachindividual user, a user identifier and attributes of a respective ATMassociated with each historical ATM transaction executed by that user;training, by the computing system using a machine learning module of thecomputing system, a plurality of prediction models, to learnindividualized user ATM habits based on training sets comprising thehistorical ATM transaction data for each user; storing in a databaseassociated with the computing system, for each user, the useridentifier, the historical ATM transaction data for the user and arespective prediction model for predicting the individualized ATM habitsof the user and the attributes of the ATMs the user frequents, whereinthe user's ATMs comprise ATMs associated with the financial institutionand third party ATMs; receiving, by the computing system from a clientdevice associated with a user, a request to locate a target ATM, whereinthe request comprises a constraint of a desired ATM, wherein thecomputing system is in electronic communication with an applicationinstalled on the client device for providing location services andelectronic dialog messaging services to the computing system, whereinthe request is generated by the user using an interface generated on theclient device by the application; determining, by the computing systemand based on the request, the user identifier associated with the userand a location of the client device; accessing, from the database andbased on the determined user identifier, the prediction model associatedwith the user; identifying, by the computing system, a plurality of ATMsproximate the location of the client device based on the constraint,wherein the plurality of identified ATMs are selected from a groupcomprising financial institution ATMs and third party ATMs; generating,by the computing system via the user's individual prediction model, apersonalized ATM recommendation for the user by: prompting, via theinterface on the client device, the user to enter parameters associatedwith the user's desired transaction; identifying, by the computingsystem, based on the user's entered parameters, current attributesassociated with each respective ATM of the plurality of ATMs proximateto the location of the client device, wherein the computing system, inresponse to the request, communicates with each respective ATM toidentify current attributes; and comparing, by the computing system, theattributes from each respective ATM of the plurality of ATMs tohistorical ATM usage statistics of the user; and interfacing, with theuser, via a dialog message session generated on the interface of theclient device and via a chat bot generated by the application, to routethe user of the client device to the target ATM from the plurality ofATMs based at least partially on the historical ATM usage statistics. 2.The method of claim 1, wherein receiving, by the computing system fromthe client device, the request to locate the target ATM comprises:receiving, by the computing system, a text message from the clientdevice; and analyzing, by the computing system, the text message toidentify the request contained therein.
 3. The method of claim 1,wherein the constraint of the desired ATM comprises at least one or moreof a maximum distance of the ATM from the location of the client device,a type of ATM, a fee-free ATM, an in-store ATM, and a no-limit ATM. 4.The method of claim 1, wherein identifying, by the computing system, theattributes associated with each respective ATM of the plurality of ATMsproximate the location of the client device comprises: receiving, fromeach ATM, a total amount of funds available in the ATM.
 5. The method ofclaim 1, wherein the attributes associated with the ATMs comprise atleast one or more of a fee associated with withdrawal, an increment inwhich funds are withdrawn, and a location of the ATM.
 6. The method ofclaim 1, routing, by the computing system, the user of the client deviceto the target ATM from the plurality of ATMs based at least partially onthe historical ATM usage statistics, comprises: routing the user to thetarget ATM, wherein the target ATM is scheduled to be refilled within apredefined period of the request.
 7. A system associated with at leastone financial institution having an ATM network, comprising: aprocessor; and a memory having programming instructions stored thereon,which, when executed by the processor, perform one or more operationscomprising: retrieving historical ATM transaction data for a pluralityof users of the ATM network, wherein the historical ATM transaction datacomprises, for each individual user, a user identifier and attributes ofa respective ATM associated with each historical ATM transactionexecuted by that user; training, using a machine learning module, aplurality of prediction models, to learn individualized user ATM habitsbased on training sets comprising the historical ATM transaction datafor each user; storing in a database, for each user, the useridentifier, the historical ATM transactions for the user and theprediction model for predicting the individualized ATM habits of theuser and the attributes of the ATMs the user frequents, wherein theuser's ATMs comprise ATMs associated with the financial institution andthird party ATMs; receiving, from a client device associated with auser, a request to locate a target ATM, wherein the request comprises aconstraint of the ATM, wherein the system is in electronic communicationwith an application installed on the client device for providinglocation services and electronic dialog messaging services to thesystem, wherein the request is generated by the user using an interfacegenerated on the client device by the application; determining, based onthe request, the user identifier associated with the user and thelocation of the client device; accessing, from the database and based onthe determined user identifier, the prediction model associated with theuser; identifying a plurality of ATMs proximate the location of theclient device based on the constraint, wherein the plurality ofidentified ATMs are selected from a group comprising financialinstitution ATMs and third party ATMs; generating, via the user'sindividual prediction model, a personalized ATM recommendation for theuser by: prompting, via the interface on the client device, the user toenter parameters associated with the user's desired transaction;identifying, based on the user's entered parameters, current attributesassociated with each respective ATM of a plurality of ATMs proximate thelocation of the client device based on the constraint, wherein thesystem, in response to the request, communicates with each respectiveATM to identify current attributes; and comparing the attributes fromeach respective ATM to historical ATM usage statistics of the user; andinterfacing, with the client device via a chat bot, to route the user toa target ATM from the plurality of ATMs based at least partially on thehistorical ATM usage statistics.
 8. The system of claim 7, whereinreceiving, from the client device, the request to locate the ATMcomprises: receiving an electronic message from the client device; andanalyzing the electronic message to identify the request containedtherein.
 9. The system of claim 7, wherein the constraint of the targetATM comprises at least one or more of a maximum distance of the ATM fromthe location of the client device, a type of ATM, a fee-free ATM, anin-store ATM, and a no-limit ATM.
 10. The system of claim 7, whereinidentifying the attributes associated with each respective ATM of theplurality of ATMs proximate the location of the client deviceidentifying, for each ATM, a total amount of funds available in the ATM.11. The system of claim 7, wherein the attributes associated with eachATM of the plurality of ATMs comprise at least one or more of a feeassociated with withdrawal, an increment in which funds are withdrawn,and a location of the ATM.
 12. A non-transitory computer readable mediumcomprising one or more sequences of instructions which, when executed byone or more processors, causes a computing system to perform operations,comprising: retrieving, by the computing system associated with at leastone financial institution having an ATM network, historical ATMtransaction data for a plurality of users of the ATM network, whereinthe historical ATM transaction data comprises, for each individual user,a user identifier and attributes of a respective ATM associated witheach historical ATM transaction executed by that user; training, by thecomputing system using a machine learning module of the computingsystem, a plurality of prediction models, to learn individualized userATM habits based on training sets comprising the historical ATMtransaction data for each user; storing in a database associated withthe computing system, for each user, the user identifier, the historicalATM transactions for the user and the prediction model for predictingthe individualized ATM habits of the user and the attributes of the ATMsthe user frequents, wherein the user's ATMs comprise ATMs associatedwith the financial institution and third party ATMs; receiving, by thecomputing system from a client device associated with a user, a requestto locate a target ATM, wherein the request comprises a constraint of adesired ATM, wherein the computing system is in electronic communicationwith an application installed on the client device for providinglocation services and electronic dialog messaging services to thecomputing system, wherein the request is generated by the user using aninterface generated on the client device by the application;determining, by the computing system and based on the request, the useridentifier associated with the user and the location of the clientdevice; accessing, from the database and based on the determined useridentifier, the prediction model associated with the user; identifying,by the computing system, a plurality of ATMs proximate the location ofthe client device based on the constraint, wherein the plurality ofidentified ATMs are selected from a group comprising financialinstitution ATMs and third party ATMs; generating, by the computingsystem via the user's individual prediction model, a personalized ATMrecommendation for the user by: prompting, via the interface on theclient device, the user to enter parameters associated with the user'sdesired transaction; identifying, by the computing system, based on theuser's entered parameters, current attributes associated with eachrespective ATM of the plurality of ATMs proximate to the location of theclient device, wherein the computing system, in response to the request,communicates with each respective ATM to identify current attributes;and comparing, by the computing system, the attributes from eachrespective ATM of the plurality of ATMs to historical ATM usagestatistics of the user; and interfacing, with the user, via a dialogmessage session generated on the interface of the client device and viaa chat bot generated by the application, to route the user of the clientdevice to the target ATM from the plurality of ATMs based at leastpartially on the historical ATM usage statistics.
 13. The non-transitorycomputer readable medium of claim 12, wherein receiving, by thecomputing system from the client device, the request to locate thetarget ATM comprises: receiving, by the computing system, a text messagefrom the client device; and analyzing, by the computing system, the textmessage to identify the request contained therein.
 14. Thenon-transitory computer readable medium of claim 12, wherein theconstraint of the desired ATM comprises at least one or more of amaximum distance of the ATM from the location of the client device, atype of ATM, a fee-free ATM, an in-store ATM, and a no-limit ATM. 15.The non-transitory computer readable medium of claim 12, whereinidentifying, by the computing system, the attributes associated witheach respective ATM of the plurality of ATMs proximate the location ofthe client device comprises: receiving, from each ATM, a total amount offunds available in the ATM.
 16. The non-transitory computer readablemedium of claim 12, wherein the attributes associated with the ATMscomprise at least one or more of a fee associated with withdrawal, anincrement in which funds are withdrawn, and a location of the ATM. 17.The non-transitory computer readable medium of claim 12, routing, by thecomputing system, the user of the client device to the target ATM fromthe plurality of ATMs based at least partially on the historical ATMusage statistics, comprises: routing the user to the target ATM, whereinthe target ATM is scheduled to be refilled within a predefined period ofthe request.